Two Major Extinction Events in the Evolutionary History of Turtles: One Caused by an Asteroid, the Other by Hominins.

AbstractWe live in a time of accelerated biological extinctions that has the potential to mirror past mass extinction events. However, the rarity of mass extinctions and the restructuring of diversity they cause complicate direct comparisons between the current extinction crisis and earlier events. Among animals, turtles (Testudinata) are one of few groups that have both a rich fossil record and sufficiently stable ecological and functional roles to enable meaningful comparisons between the end-Cretaceous mass extinction (∼66 Ma) and the ongoing wave of extinctions. Here we analyze the fossil record of the entire turtle clade and identify two peaks in extinction rates over their evolutionary history. The first coincides with the Cretaceous-Paleogene transition, reflecting patterns previously reported for other taxa. The second major extinction event started in the Pliocene and continues until now. This peak is detectable only for terrestrial turtles and started much earlier in Africa and Eurasia than elsewhere. On the basis of the timing, geography, and functional group of this extinction event, we postulate a link to co-occurring hominins rather than climate change as the cause. These results lend further support to the view that negative biodiversity impacts were already incurred by our ancestors and related lineages and demonstrate the severity of this continued impact through human activities.

DeepDive: estimating global biodiversity patterns through time using deep learning.

Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a >70% diversity drop in the Pleistocene.

The Clade Replacement Theory: a framework to study age-dependent extinction.

There is no scientific consensus about whether and how species' evolutionary age, or the elapsed time since their origination, might affect their probability of going extinct. Different age-dependent extinction (ADE) patterns have been proposed in theoretical and empirical studies, while the existence of a consistent and universal pattern across the tree of life remains debated. If evolutionary age predicts species extinction probability, then the study of ADE should comprise the elapsed time and the ecological process acting on species from their origin to their extinction or to the present for extant species. Additionally, given that closely related species share traits associated with fitness, evolutionary proximity could generate similar ADE patterns. Considering the historical context and extinction selectivity based on evolutionary relatedness, we build on previous theoretical work to formalize the Clade Replacement Theory (CRT) as a framework that considers the ecological and evolutionary aspects of species age and extinction probability to produce testable predictions on ADE patterns. CRT's domain is the diversification dynamics of two or more clades competing for environmental space throughout time, and its propositions or derived hypotheses are as follows: (i) incumbency effects by an early arriving clade that limit the colonization and the diversification of a younger clade leading to a negative ADE scenario (younger species more prone to extinction than older ones) and (ii) an ecological shift triggered by an environmental change that imposes a new selective regime over the environmental space and leads to a positive ADE scenario (extinction probability increasing with age). From these propositions, we developed the prediction that the ADE scenario would be defined by whether an ecological shift happens or not. We discuss how the CRT could be tested with empirical data and provide examples where it could be applied. We hope this article will provide a common ground to unify results from different fields and foster new empirical tests of the mechanisms derived here while providing insights into CRT theoretical structuration.

Functional diversity of sharks and rays is highly vulnerable and supported by unique species and locations worldwide.

Elasmobranchs (sharks, rays and skates) are among the most threatened marine vertebrates, yet their global functional diversity remains largely unknown. Here, we use a trait dataset of >1000 species to assess elasmobranch functional diversity and compare it against other previously studied biodiversity facets (taxonomic and phylogenetic), to identify species- and spatial- conservation priorities. We show that threatened species encompass the full extent of functional space and disproportionately include functionally distinct species. Applying the conservation metric FUSE (Functionally Unique, Specialised, and Endangered) reveals that most top-ranking species differ from the top Evolutionarily Distinct and Globally Endangered (EDGE) list. Spatial analyses further show that elasmobranch functional richness is concentrated along continental shelves and around oceanic islands, with 18 distinguishable hotspots. These hotspots only marginally overlap with those of other biodiversity facets, reflecting a distinct spatial fingerprint of functional diversity. Elasmobranch biodiversity facets converge with fishing pressure along the coast of China, which emerges as a critical frontier in conservation. Meanwhile, several components of elasmobranch functional diversity fall in high seas and/or outside the global network of marine protected areas. Overall, our results highlight acute vulnerability of the world's elasmobranchs' functional diversity and reveal global priorities for elasmobranch functional biodiversity previously overlooked.

Ontogeny and phylogeny of mating behaviour: social heteroch rony in primates.

Based on the Recognition Concept of species, the specific-mate contact model posits that mating systems develop as combinations of two fundamental courtship strategies that we interpret here in terms of behavioural heterochrony: territorial mate-attraction evolved as an effect of peramorphosis whereas group-living mate-seeking evolved as an effect of paedomorphosis. We tested this hypothesis on primates in a phylogenetic and paleo-climatic context. Our results suggest that primate promiscuity (both males and females are mate-seekers) evolved with group-living from ancestral pair-living monogamy (both males and females are mate-attractors) in the Palaeogene, as the result of a slowdown in growth (neoteny) caused by increased environmental predictability. A secondary return to territorial monogamy probably evolved as the result of accelerated growth driven by seasonality (acceleration). Polygamy evolved in the Neogene during periods of forest fragmentation and environmental unpredictability. Small monogamous ancestors evolved seasonal polyandry (female attraction) as an effect of truncated development (progenesis). Large promiscuous, neotenic ancestors evolved non-seasonal polygyny (male attraction) as an effect of prolonged development (hypermorphosis) in males. We conclude that social heterochrony offers alternative explanations for the coevolution of life history and mating be-haviour; and we discuss the implications of our model for human social evolution.

Conceptual and empirical bridges between micro- and macroevolution.

Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years.

A timescale for placental mammal diversification based on Bayesian modeling of the fossil record.

The timing of the placental mammal radiation has been the focus of debate over the efficacy of competing methods for establishing evolutionary timescales. Molecular clock analyses estimate that placental mammals originated before the Cretaceous-Paleogene (K-Pg) mass extinction, anywhere from the Late Cretaceous to the Jurassic. However, the absence of definitive fossils of placentals before the K-Pg boundary is compatible with a post-Cretaceous origin. Nevertheless, lineage divergence must occur before it can be manifest phenotypically in descendent lineages. This, combined with the non-uniformity of the rock and fossil records, requires the fossil record to be interpreted rather than read literally. To achieve this, we introduce an extended Bayesian Brownian bridge model that estimates the age of origination and, where applicable, extinction through a probabilistic interpretation of the fossil record. The model estimates the origination of placentals in the Late Cretaceous, with ordinal crown groups originating at or after the K-Pg boundary. The results reduce the plausible interval for placental mammal origination to the younger range of molecular clock estimates. Our findings support both the Long Fuse and Soft Explosive models of placental mammal diversification, indicating that the placentals originated shortly prior to the K-Pg mass extinction. The origination of many modern mammal lineages overlapped with and followed the K-Pg mass extinction.

Patterns and drivers of heat production in the plant genus Amorphophallus.

Thermogenesis - the ability to generate metabolic heat - is much more common in animals than in plants, but it has been documented in several plant families, most prominently the Araceae. Metabolic heat is produced in floral organs during the flowering time (anthesis), with the hypothesised primary functions being to increase scent volatilisation for pollinator attraction, and/or to provide a heat reward for invertebrate pollinators. Despite in-depth studies on the thermogenesis of single species, no attempts have yet been made to examine plant thermogenesis across an entire clade. Here, we apply time-series clustering algorithms to 119 measurements of the full thermogenic patterns in inflorescences of 80 Amorphophallus species. We infer a new time-calibrated phylogeny of this genus and use phylogenetic comparative methods to investigate the evolutionary determinants of thermogenesis. We find striking phenotypic variation across the phylogeny, with heat production in multiple clades reaching up to 15°C, and in one case 21.7°C above ambient temperature. Our results show that the thermogenic capacity is phylogenetically conserved and is also associated with inflorescence thickness. Our study paves the way for further investigations of the eco-evolutionary benefits of thermogenesis in plants.

Machine learning enhances prediction of plants as potential sources of antimalarials.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families - Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) - and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms - Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks - and compare these to two ethnobotanical selection approaches - based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best - attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

Dwarfism and gigantism drive human-mediated extinctions on islands.

Islands have long been recognized as distinctive evolutionary arenas leading to morphologically divergent species, such as dwarfs and giants. We assessed how body size evolution in island mammals may have exacerbated their vulnerability, as well as how human arrival has contributed to their past and ongoing extinctions, by integrating data on 1231 extant and 350 extinct species from islands and paleo islands worldwide spanning the past 23 million years. We found that the likelihood of extinction and of endangerment are highest in the most extreme island dwarfs and giants. Extinction risk of insular mammals was compounded by the arrival of modern humans, which accelerated extinction rates more than 10-fold, resulting in an almost complete demise of these iconic marvels of island evolution.

Light, rather than circadian rhythm, regulates gas exchange in ferns and lycophytes.

Circadian regulation plays a vital role in optimizing plant responses to the environment. However, while circadian regulation has been extensively studied in angiosperms, very little is known for lycophytes and ferns, leaving a gap in our understanding of the evolution of circadian rhythms across the plant kingdom. Here, we investigated circadian regulation in gas exchange through stomatal conductance and photosynthetic efficiency in a phylogenetically broad panel of 21 species of lycophytes and ferns over a 46 h period under constant light and a selected few under more natural conditions with day-night cycles. No rhythm was detected under constant light for either lycophytes or ferns, except for two semi-aquatic species of the family Marsileaceae (Marsilea azorica and Regnellidium diphyllum), which showed rhythms in stomatal conductance. Furthermore, these results indicated the presence of a light-driven stomatal control for ferns and lycophytes, with a possible passive fine-tuning through leaf water status adjustments. These findings support previous evidence for the fundamentally different regulation of gas exchange in lycophytes and ferns compared to angiosperms, and they suggest the presence of alternative stomatal regulations in Marsileaceae, an aquatic family already well known for numerous other distinctive physiological traits. Overall, our study provides evidence for heterogeneous circadian regulation across plant lineages, highlighting the importance of broad taxonomic scope in comparative plant physiology studies.

Slow and steady wins the race: Diversification rate is independent from body size and lifestyle in Malagasy skinks (Squamata: Scincidae: Scincinae).

Most of the unique and diverse vertebrate fauna that inhabits Madagascar derives from in situ diversification from colonisers that reached this continental island through overseas dispersal. The endemic Malagasy Scincinae lizards are amongst the most species-rich squamate groups on the island. They colonised all bioclimatic zones and display many ecomorphological adaptations to a fossorial (burrowing) lifestyle. Here we propose a new phylogenetic hypothesis for their diversification based on the largest taxon sampling so far compiled for this group. We estimated divergence times and investigated several aspects of their diversification (diversification rate, body size and fossorial lifestyle evolution, and biogeography). We found that diversification rate was constant throughout most of the evolutionary history of the group, but decreased over the last 6-4 million years and independently from body size and fossorial lifestyle evolution. Fossoriality has evolved from fully quadrupedal ancestors at least five times independently, which demonstrates that even complex morphological syndromes - in this case involving traits such as limb regression, body elongation, modification of cephalic scalation, depigmentation, and eyes and ear-opening regression - can evolve repeatedly and independently given enough time and eco-evolutionary advantages. Initial diversification of the group likely occurred in forests, and the divergence of sand-swimmer genera around 20 Ma appears linked to a period of aridification. Our results show that the large phenotypic variability of Malagasy Scincinae has not influenced diversification rate and that their rich species diversity results from a constant accumulation of lineages through time. By compiling large geographic and trait-related datasets together with the computation of a new time tree for the group, our study contributes important insights on the diversification of Malagasy vertebrates.

The origin of modern patterns of continental diversity in Mauritiinae palms: the Neotropical museum and the Afrotropical graveyard.

While the latitudinal diversity gradient has received much attention, biodiversity and species richness also vary between continents across similar latitudes. Fossil information can be used to understand the evolutionary mechanisms that generated such variation between continents of similar latitudes. We integrated fossil data into a phylogenetic analysis of the Mauritiinae palms, whose extant diversity is restricted to the Neotropics, but extended across Africa and India during most of the Cenozoic. Mauritiinae diverged from its sister lineage Raphiinae ca 106 Ma. Using ancestral state estimation and a lineage through time analysis, we found that diversity arose globally during the late Cretaceous and Palaeocene across South America, Africa and India. The Palaeocene-Eocene transition (ca 56 Ma) marked the end of global Mauritiinae expansion, and the beginning of their decline in both Africa and India. Mauritiinae disappeared from the Indian subcontinent and Africa at the end of the Eocene and the Miocene, respectively. By contrast, Neotropical diversity steadily increased over the last 80 Myr. Taken together, our results suggest that the Neotropics functioned as a continental-scale refuge for Mauritiinae palms, where lineages survived and diversified while global climatic changes that drastically reduced rainforests led to their demise on other continents.

Short-term paleogeographic reorganizations and climate events shaped diversification of North American freshwater gastropods over deep time.

What controls species diversity and diversification is one of the major questions in evolutionary biology and paleontology. Previous studies have addressed this issue based on various plant and animal groups, geographic regions, and time intervals. However, as most previous research focused on terrestrial or marine ecosystems, our understanding of the controls on diversification of biota (and particularly invertebrates) in freshwater environments in deep time is still limited. Here, we infer diversification rates of North American freshwater gastropods from the Late Triassic to the Pleistocene and explore potential links between shifts in speciation and extinction and major changes in paleogeography, climate, and biotic interactions. We found that variation in the speciation rate is best explained by changes in continental fragmentation, with rate shifts coinciding with major paleogeographic reorganizations in the Mesozoic, in particular the retreat of the Sundance Sea and subsequent development of the Bighorn wetland and the advance of the Western Interior Seaway. Climatic events in the Cenozoic (Middle Eocene Climate Optimum, Miocene Climate Optimum) variably coincide with shifts in speciation and extinction as well, but no significant long-term association could be detected. Similarly, no influence of diversity dependence was found across the entire time frame of ~ 214 Myr. Our results indicate that short-term climatic events and paleogeographic changes are relevant to the diversification of continental freshwater biota, while long-term trends have limited effect.

The origin and evolution of open habitats in North America inferred by Bayesian deep learning models.

Some of the most extensive terrestrial biomes today consist of open vegetation, including temperate grasslands and tropical savannas. These biomes originated relatively recently in Earth's history, likely replacing forested habitats in the second half of the Cenozoic. However, the timing of their origination and expansion remains disputed. Here, we present a Bayesian deep learning model that utilizes information from fossil evidence, geologic models, and paleoclimatic proxies to reconstruct paleovegetation, placing the emergence of open habitats in North America at around 23 million years ago. By the time of the onset of the Quaternary glacial cycles, open habitats were covering more than 30% of North America and were expanding at peak rates, to eventually become the most prominent natural vegetation type today. Our entirely data-driven approach demonstrates how deep learning can harness unexplored signals from complex data sets to provide insights into the evolution of Earth's biomes in time and space.

Global diversity dynamics in the fossil record are regionally heterogeneous.

Global diversity patterns in the fossil record comprise a mosaic of regional trends, underpinned by spatially non-random drivers and distorted by variation in sampling intensity through time and across space. Sampling-corrected diversity estimates from spatially-standardised fossil datasets retain their regional biogeographic nuances and avoid these biases, yet diversity-through-time arises from the interplay of origination and extinction, the processes that shape macroevolutionary history. Here we present a subsampling algorithm to eliminate spatial sampling bias, coupled with advanced probabilistic methods for estimating origination and extinction rates and a Bayesian method for estimating sampling-corrected diversity. We then re-examine the Late Permian to Early Jurassic marine fossil record, an interval spanning several global biotic upheavals that shaped the origins of the modern marine biosphere. We find that origination and extinction rates are regionally heterogenous even during events that manifested globally, highlighting the need for spatially explicit views of macroevolutionary processes through geological time.

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

Improving biodiversity protection through artificial intelligence.

Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.

Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence.

Trees are fundamental for Earth's biodiversity as primary producers and ecosystem engineers and are responsible for many of nature's contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk-the Red List of the International Union for the Conservation of Nature (IUCN RL)-lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species' geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species' IUCN RL extinction risk assessments.

Climate warming can reduce biocontrol efficacy and promote plant invasion due to both genetic and transient metabolomic changes.

Climate change may affect plant-herbivore interactions and their associated ecosystem functions. In an experimental evolution approach, we subjected replicated populations of the invasive Ambrosia artemisiifolia to a combination of simulated warming and herbivory by a potential biocontrol beetle. We tracked genomic and metabolomic changes across generations in field populations and assessed plant offspring phenotypes in a common environment. Using an integrated Bayesian model, we show that increased offspring biomass in response to warming arose through changes in the genetic composition of populations. In contrast, increased resistance to herbivory arose through a shift in plant metabolomic profiles without genetic changes, most likely by transgenerational induction of defences. Importantly, while increased resistance was costly at ambient temperatures, warming removed this constraint and favoured both vigorous and better defended plants under biocontrol. Climate warming may thus decrease biocontrol efficiency and promote Ambrosia invasion, with potentially serious economic and health consequences.